Extra-freedom stitch method for reference line smoothing

ABSTRACT

In one embodiment, a method for generating a reference line for operating an autonomous driving vehicle includes controlling an autonomous driving vehicle to move along a road according to a first reference line, the first reference line having a first set of constraints, and while the autonomous driving vehicle is moving along the road according to the first reference line: truncating the first reference line by removing an end section of the first reference line to generate a truncated first reference line including an end reference point, obtaining a second set of constraints for the end reference point, obtaining a second reference line to be used by the autonomous driving vehicle, the second reference line having the first set of constraints, and connecting the second reference line to the end reference point of the truncated reference line to allow the autonomous driving vehicle to move along the road.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to operatingautonomous vehicles. More particularly, embodiments of the disclosurerelate to generating references lines for autonomous driving vehicles.

BACKGROUND

Vehicles operating in an autonomous mode (e.g., driverless) can relieveoccupants, especially the driver, from some driving-relatedresponsibilities. When operating in an autonomous mode, the vehicle cannavigate to various locations using onboard sensors, allowing thevehicle to travel with minimal human interaction or in some caseswithout any passengers.

Motion planning and control are critical operations in autonomousdriving. Typically, an autonomous driving vehicle (ADV) is controlledand driven according to a reference line. When generating a drivingtrajectory, the system heavily relies on the reference line. Thereference line is a smooth line on the map. The vehicle tries to driveby following the reference line. Roads and lanes on the map are oftenrepresented by a list of connected line segments, which are not smoothand difficult for the ADV to follow. As a result, a smooth optimizationis performed on the reference line to smooth the reference line.However, such optimization may not necessarily yield a smooth referenceline.

It's necessary to generate a smooth reference line, especially when theADV is travelling at a high speed. While the reference line length/timerequired is usually not linear but exponential, the reference line needsto be smoothed separately and stitched together. However, in manysmoothing algorithms (e.g., Quadratic Programming), limiting its highlevel derivative (e.g., curvature and curvature derivative) isdifficult, and makes the stitch point between two reference linesdiscontinuous especially when there is a large gap between two adjacentcurvatures that need to be joined.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure are illustrated by way of example and notlimitation in the figures of the accompanying drawings in which likereferences indicate similar elements.

FIG. 1 is a block diagram illustrating a networked system according toone embodiment.

FIG. 2 is a block diagram illustrating an example of an autonomousvehicle according to one embodiment.

FIGS. 3A-3B are block diagrams illustrating an example of a perceptionand planning system used with an autonomous vehicle according to oneembodiment.

FIG. 4 is a diagram illustrating a process of connecting reference linesaccording to one embodiment.

FIG. 5 is a flow diagram illustrating a process of generating areference line for controlling an autonomous driving vehicle accordingto one embodiment.

FIG. 6 is a block diagram illustrating a data processing systemaccording to one embodiment.

DETAILED DESCRIPTION

Various embodiments and aspects of the disclosures will be describedwith reference to details discussed below, and the accompanying drawingswill illustrate the various embodiments. The following description anddrawings are illustrative of the disclosure and are not to be construedas limiting the disclosure. Numerous specific details are described toprovide a thorough understanding of various embodiments of the presentdisclosure. However, in certain instances, well-known or conventionaldetails are not described in order to provide a concise discussion ofembodiments of the present disclosures.

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin conjunction with the embodiment can be included in at least oneembodiment of the disclosure. The appearances of the phrase “in oneembodiment” in various places in the specification do not necessarilyall refer to the same embodiment.

According to some embodiments, an autonomous driving vehicle (also knownas “autonomous vehicle”) includes a decision and planning system forcontrolling an autonomous driving vehicle to move along a road accordingto a first reference line, the first reference line having a first setof constraints. And while the autonomous driving vehicle is moving alongthe road according to the first reference line, a reference linegenerator of the autonomous driving vehicle truncates the firstreference line by removing an end section of the first reference line togenerate a truncated first reference line including an end referencepoint, obtains a second set of constraints for the end reference point,obtains a second reference line to be used by the autonomous drivingvehicle, the second reference line having the first set of constraints,and connects the second reference line to the end reference point of thetruncated reference line to allow the autonomous driving vehicle tocontinue to move along the road to reach its destination location. Inone aspect of the present disclosure, the end section removed from thefirst reference line and successive reference lines is generally aboutten percent of a total length of the reference line such as the firstreference line.

FIG. 1 is a block diagram illustrating an autonomous vehicle networkconfiguration according to one embodiment of the disclosure. Referringto FIG. 1, network configuration 100 includes autonomous driving vehicle(ADV) 101 that may be communicatively coupled to one or more servers103-104 over a network 102. Although there is one autonomous vehicleshown, multiple autonomous vehicles can be coupled to each other and/orcoupled to servers 103-104 over network 102. Network 102 may be any typeof networks such as a local area network (LAN), a wide area network(WAN) such as the Internet, a cellular network, a satellite network, ora combination thereof, wired or wireless. Server(s) 103-104 may be anykind of servers or a cluster of servers, such as Web or cloud servers,application servers, backend servers, or a combination thereof. Servers103-104 may be data analytics servers, content servers, trafficinformation servers, map and point of interest (MPOI) servers, orlocation servers, etc.

An autonomous vehicle refers to a vehicle that can be configured to inan autonomous mode in which the vehicle navigates through an environmentwith little or no input from a driver. Such an autonomous vehicle caninclude a sensor system having one or more sensors that are configuredto detect information about the environment in which the vehicleoperates. The vehicle and its associated controller(s) use the detectedinformation to navigate through the environment. Autonomous vehicle 101can operate in a manual mode, a full autonomous mode, or a partialautonomous mode.

In one embodiment, autonomous vehicle 101 includes, but is not limitedto, perception and planning system 110, vehicle control system 111,wireless communication system 112, user interface system 113,infotainment system 114, and sensor system 115. Autonomous vehicle 101may further include certain common components included in ordinaryvehicles, such as, an engine, wheels, steering wheel, transmission,etc., which may be controlled by vehicle control system 111 and/orperception and planning system 110 using a variety of communicationsignals and/or commands, such as, for example, acceleration signals orcommands, deceleration signals or commands, steering signals orcommands, braking signals or commands, etc.

Components 110-115 may be communicatively coupled to each other via aninterconnect, a bus, a network, or a combination thereof. For example,components 110-115 may be communicatively coupled to each other via acontroller area network (CAN) bus. A CAN bus is a vehicle bus standarddesigned to allow microcontrollers and devices to communicate with eachother in applications without a host computer. It is a message-basedprotocol, designed originally for multiplex electrical wiring withinautomobiles, but is also used in many other contexts.

Referring now to FIG. 2, in one embodiment, sensor system 115 includes,but it is not limited to, one or more cameras 211, global positioningsystem (GPS) unit 212, inertial measurement unit (IMU) 213, radar unit214, and a light detection and range (LIDAR) unit 215. GPS system 212may include a transceiver operable to provide information regarding theposition of the autonomous vehicle. IMU unit 213 may sense position andorientation changes of the autonomous vehicle based on inertialacceleration. Radar unit 214 may represent a system that utilizes radiosignals to sense objects within the local environment of the autonomousvehicle. In some embodiments, in addition to sensing objects, radar unit214 may additionally sense the speed and/or heading of the objects.LIDAR unit 215 may sense objects in the environment in which theautonomous vehicle is located using lasers. LIDAR unit 215 could includeone or more laser sources, a laser scanner, and one or more detectors,among other system components. Cameras 211 may include one or moredevices to capture images of the environment surrounding the autonomousvehicle. Cameras 211 may be still cameras and/or video cameras. A cameramay be mechanically movable, for example, by mounting the camera on arotating and/or tilting a platform.

Sensor system 115 may further include other sensors, such as, a sonarsensor, an infrared sensor, a steering sensor, a throttle sensor, abraking sensor, and an audio sensor (e.g., microphone). An audio sensormay be configured to capture sound from the environment surrounding theautonomous vehicle. A steering sensor may be configured to sense thesteering angle of a steering wheel, wheels of the vehicle, or acombination thereof. A throttle sensor and a braking sensor sense thethrottle position and braking position of the vehicle, respectively. Insome situations, a throttle sensor and a braking sensor may beintegrated as an integrated throttle/braking sensor.

In one embodiment, vehicle control system 111 includes, but is notlimited to, steering unit 201, throttle unit 202 (also referred to as anacceleration unit), and braking unit 203. Steering unit 201 is to adjustthe direction or heading of the vehicle. Throttle unit 202 is to controlthe speed of the motor or engine that in turn control the speed andacceleration of the vehicle. Braking unit 203 is to decelerate thevehicle by providing friction to slow the wheels or tires of thevehicle. Note that the components as shown in FIG. 2 may be implementedin hardware, software, or a combination thereof.

Referring back to FIG. 1, wireless communication system 112 is to allowcommunication between autonomous vehicle 101 and external systems, suchas devices, sensors, other vehicles, etc. For example, wirelesscommunication system 112 can wirelessly communicate with one or moredevices directly or via a communication network, such as servers 103-104over network 102. Wireless communication system 112 can use any cellularcommunication network or a wireless local area network (WLAN), e.g.,using WiFi to communicate with another component or system. Wirelesscommunication system 112 could communicate directly with a device (e.g.,a mobile device of a passenger, a display device, a speaker withinvehicle 101), for example, using an infrared link, Bluetooth, etc. Userinterface system 113 may be part of peripheral devices implementedwithin vehicle 101 including, for example, a keyboard, a touch screendisplay device, a microphone, and a speaker, etc.

Some or all of the functions of autonomous vehicle 101 may be controlledor managed by perception and planning system 110, especially whenoperating in an autonomous driving mode. Perception and planning system110 includes the necessary hardware (e.g., processor(s), memory,storage) and software (e.g., operating system, planning and routingprograms) to receive information from sensor system 115, control system111, wireless communication system 112, and/or user interface system113, process the received information, plan a route or path from astarting point to a destination point, and then drive vehicle 101 basedon the planning and control information. Alternatively, perception andplanning system 110 may be integrated with vehicle control system 111.

For example, a user as a passenger may specify a starting location and adestination of a trip, for example, via a user interface. Perception andplanning system 110 obtains the trip related data. For example,perception and planning system 110 may obtain location and routeinformation from an MPOI server, which may be a part of servers 103-104.The location server provides location services and the MPOI serverprovides map services and the POIs of certain locations. Alternatively,such location and MPOI information may be cached locally in a persistentstorage device of perception and planning system 110.

While autonomous vehicle 101 is moving along the route, perception andplanning system 110 may also obtain real-time traffic information from atraffic information system or server (TIS). Note that servers 103-104may be operated by a third party entity. Alternatively, thefunctionalities of servers 103-104 may be integrated with perception andplanning system 110. Based on the real-time traffic information, MPOIinformation, and location information, as well as real-time localenvironment data detected or sensed by sensor system 115 (e.g.,obstacles, objects, nearby vehicles), perception and planning system 110can plan an optimal route and drive vehicle 101, for example, viacontrol system 111, according to the planned route to reach thespecified destination safely and efficiently.

Server 103 may be a data analytics system to perform data analyticsservices for a variety of clients. In one embodiment, data analyticssystem 103 includes data collector 121 and machine learning engine 122.Data collector 121 collects driving statistics 123 from a variety ofvehicles, either autonomous vehicles or regular vehicles driven by humandrivers. Driving statistics 123 include information indicating thedriving commands (e.g., throttle, brake, steering commands) issued andresponses of the vehicles (e.g., speeds, accelerations, decelerations,directions) captured by sensors of the vehicles at different points intime. Driving statistics 123 may further include information describingthe driving environments at different points in time, such as, forexample, routes (including starting and destination locations), MPOIs,road conditions, weather conditions, etc.

Based on driving statistics 123, machine learning engine 122 generatesor trains a set of rules, algorithms, and/or predictive models 124 for avariety of purposes. For example, algorithms 124 may include analgorithm or model to generate a smooth reference line. Algorithms 124can then be uploaded on ADVs to be utilized during autonomous driving inreal-time.

FIGS. 3A and 3B are block diagrams illustrating an example of aperception and planning system used with an autonomous vehicle accordingto one embodiment. System 300 may be implemented as a part of autonomousvehicle 101 of FIG. 1 including, but is not limited to, perception andplanning system 110, control system 111, and sensor system 115.Referring to FIGS. 3A-3B, perception and planning system 110 includes,but is not limited to, localization module 301, perception module 302,prediction module 303, decision module 304, planning module 305, controlmodule 306, routing module 307, and reference line generator 308.

Some or all of modules 301-308 may be implemented in software, hardware,or a combination thereof. For example, these modules may be installed inpersistent storage device 352, loaded into memory 351, and executed byone or more processors (not shown). Note that some or all of thesemodules may be communicatively coupled to or integrated with some or allmodules of vehicle control system 111 of FIG. 2. Some of modules 301-308may be integrated together as an integrated module.

Localization module 301 determines a current location of autonomousvehicle 300 (e.g., leveraging GPS unit 212) and manages any data relatedto a trip or route of a user. Localization module 301 (also referred toas a map and route module) manages any data related to a trip or routeof a user. A user may log in and specify a starting location and adestination of a trip, for example, via a user interface. Localizationmodule 301 communicates with other components of autonomous vehicle 300,such as map and route information 311, to obtain the trip related data.For example, localization module 301 may obtain location and routeinformation from a location server and a map and POI (MPOI) server. Alocation server provides location services and an MPOI server providesmap services and the POIs of certain locations, which may be cached aspart of map and route information 311. While autonomous vehicle 300 ismoving along the route, localization module 301 may also obtainreal-time traffic information from a traffic information system orserver.

Based on the sensor data provided by sensor system 115 and localizationinformation obtained by localization module 301, a perception of thesurrounding environment is determined by perception module 302. Theperception information may represent what an ordinary driver wouldperceive surrounding a vehicle in which the driver is driving. Theperception can include the lane configuration, traffic light signals, arelative position of another vehicle, a pedestrian, a building,crosswalk, or other traffic related signs (e.g., stop signs, yieldsigns), etc., for example, in a form of an object. The laneconfiguration includes information describing a lane or lanes, such as,for example, a shape of the lane (e.g., straight or curvature), a widthof the lane, how many lanes in a road, one-way or two-way lane, mergingor splitting lanes, exiting lane, etc.

Perception module 302 may include a computer vision system orfunctionalities of a computer vision system to process and analyzeimages captured by one or more cameras in order to identify objectsand/or features in the environment of autonomous vehicle. The objectscan include traffic signals, road way boundaries, other vehicles,pedestrians, and/or obstacles, etc. The computer vision system may usean object recognition algorithm, video tracking, and other computervision techniques. In some embodiments, the computer vision system canmap an environment, track objects, and estimate the speed of objects,etc. Perception module 302 can also detect objects based on othersensors data provided by other sensors such as a radar and/or LIDAR.

For each of the objects, prediction module 303 predicts what the objectwill behave under the circumstances. The prediction is performed basedon the perception data perceiving the driving environment at the pointin time in view of a set of map/route information 311 and traffic rules312. For example, if the object is a vehicle at an opposing directionand the current driving environment includes an intersection, predictionmodule 303 will predict whether the vehicle will likely move straightforward or make a turn. If the perception data indicates that theintersection has no traffic light, prediction module 303 may predictthat the vehicle may have to fully stop prior to enter the intersection.If the perception data indicates that the vehicle is currently at aleft-turn only lane or a right-turn only lane, prediction module 303 maypredict that the vehicle will more likely make a left turn or right turnrespectively.

For each of the objects, decision module 304 makes a decision regardinghow to handle the object. For example, for a particular object (e.g.,another vehicle in a crossing route) as well as its metadata describingthe object (e.g., a speed, direction, turning angle), decision module304 decides how to encounter the object (e.g., overtake, yield, stop,pass). Decision module 304 may make such decisions according to a set ofrules such as traffic rules or driving rules 312, which may be stored inpersistent storage device 352.

Routing module 307 is configured to provide one or more routes or pathsfrom a starting point to a destination point. For a given trip from astart location to a destination location, for example, received from auser, routing module 307 obtains route and map information 311 anddetermines all possible routes or paths from the starting location toreach the destination location. Routing module 307 may generate areference line in a form of a topographic map for each of the routes itdetermines from the starting location to reach the destination location.A reference line refers to an ideal route or path without anyinterference from others such as other vehicles, obstacles, or trafficcondition. That is, if there is no other vehicle, pedestrians, orobstacles on the road, an ADV should exactly or closely follow thereference line. The topographic maps are then provided to decisionmodule 304 and/or planning module 305. Decision module 304 and/orplanning module 305 examine all of the possible routes to select andmodify one of the most optimal routes in view of other data provided byother modules such as traffic conditions from localization module 301,driving environment perceived by perception module 302, and trafficcondition predicted by prediction module 303. The actual path or routefor controlling the ADV may be close to or different from the referenceline provided by routing module 307 dependent upon the specific drivingenvironment at the point in time.

Based on a decision for each of the objects perceived, planning module305 plans a path or route for the autonomous vehicle, as well as drivingparameters (e.g., distance, speed, and/or turning angle), using areference line provided by routing module 307 or reference linegenerator 308 as a basis. That is, for a given object, decision module304 decides what to do with the object, while planning module 305determines how to do it. For example, for a given object, decisionmodule 304 may decide to pass the object, while planning module 305 maydetermine whether to pass on the left side or right side of the object.Planning and control data is generated by planning module 305 includinginformation describing how vehicle 300 would move in a next moving cycle(e.g., next route/path segment). For example, the planning and controldata may instruct vehicle 300 to move 10 meters at a speed of 30 mileper hour (mph), then change to a right lane at the speed of 25 mph.

Based on the planning and control data, control module 306 controls anddrives the autonomous vehicle, by sending proper commands or signals tovehicle control system 111, according to a route or path defined by theplanning and control data. The planning and control data includesufficient information to drive the vehicle from a first point to asecond point of a route or path using appropriate vehicle settings ordriving parameters (e.g., throttle, braking, steering commands) atdifferent points in time along the path or route.

In one embodiment, the planning phase is performed in a number ofplanning cycles, also referred to as driving cycles, such as, forexample, in every time interval of 100 milliseconds (ms). For each ofthe planning cycles or driving cycles, one or more control commands willbe issued based on the planning and control data. That is, for every 100ms, planning module 305 plans a next route segment or path segment, forexample, including a target position and the time required for the ADVto reach the target position. Alternatively, planning module 305 mayfurther specify the specific speed, direction, and/or steering angle,etc. In one embodiment, planning module 305 plans a route segment orpath segment for the next predetermined period of time such as 5seconds. For each planning cycle, planning module 305 plans a targetposition for the current cycle (e.g., next 5 seconds) based on a targetposition planned in a previous cycle. Control module 306 then generatesone or more control commands (e.g., throttle, brake, steering controlcommands) based on the planning and control data of the current cycle.

Note that decision module 304 and planning module 305 may be integratedas an integrated module. Decision module 304/planning module 305 mayinclude a navigation system or functionalities of a navigation system todetermine a driving path for the autonomous vehicle. For example, thenavigation system may determine a series of speeds and directionalheadings to affect movement of the autonomous vehicle along a path thatsubstantially avoids perceived obstacles while generally advancing theautonomous vehicle along a roadway-based path leading to an ultimatedestination. The destination may be set according to user inputs viauser interface system 113. The navigation system may update the drivingpath dynamically while the autonomous vehicle is in operation. Thenavigation system can incorporate data from a GPS system and one or moremaps so as to determine the driving path for the autonomous vehicle.

Continuing with FIGS. 3A and 3B, reference line generator 308 generatesone or more reference lines for operating an ADV. Note that referenceline generator 308 may be integrated with routing module 307 or it mayexist as a separate module. According to one embodiment, when an initialreference line has been determined and received from routing module 307,reference line generator 308 is configured to generate a first referenceline. Typically, the initial reference line was created based on theroute and map information. A road is typically represented by a sequenceof road segments. An initial reference line is typically the center lineof each road segment. As a result, the initial reference line is acollection of center line segments connected to each other and such aninitial reference line is not smooth, particularly at the joint of twoadjacent segments. Based on the initial reference line, reference linegenerator 308 performs an optimization on the initial reference line togenerate a smooth reference line.

In one embodiment, reference line generator 308 performs a splineoptimization on the selected control points of the initial referenceline. A spline is a curve represented by one or more (e.g., piecewise)polynomials joined together to form the curve. For example, a polynomialor a polynomial function can represent a segment between adjacentcontrol points. Each control point is associated with a set ofconstraints, which include initial constraints, equality constraints,and inequality constraints.

The initial constraints include a set of constraints corresponding tothe ADV's initial condition, e.g., ADV's immediate direction and/orgeographical location. Equality constraints include a set of equalityconstraints asserting some equality conditions that must be satisfied.For example, the equality constraints can include a set of constraintsthat guarantee joint smoothness and/or some pointwise constraints aresatisfied (e.g., the spline will pass some points or have some specificpoint heading). The inequality constraints include a set of constraintsthat guarantee the spline is within some boundary (e.g., less than orgreater than some constraint value). Both inequality and equalityconstraints are hard constraints, meaning that it is required that theyare satisfied. The resulting reference line will be smooth.

According to one embodiment, an initial reference line can be generatedusing dynamic programming techniques. Such a reference line may bereferred to as a rough reference line, which is not smooth. Dynamicprogramming (or dynamic optimization) is a mathematical optimizationmethod that breaks down a problem to be solved into a sequence of valuefunctions, solving each of these value functions just once and storingtheir solutions. The next time the same value function occurs, theprevious computed solution is simply looked up saving computation timeinstead of recomputing its solution. Once the initial or rough referenceline has been generated, the initial reference line may be smoothed byan optimization process. In one embodiment, the reference line smoothoptimization is performed using quadratic programming techniques.Quadratic programming involves minimizing or maximizing an objectivefunction (e.g., a quadratic function with several variables) subject tobounds, linear equality, and/or inequality constraints. One differencebetween dynamic programming and quadratic programming is that quadraticprogramming optimizes all candidate movements for all points on thereference line at once.

The term of polynomial optimization or polynomial fit refers to theoptimization of the shape of a curve (in this example, a trajectory)represented by a polynomial function (e.g., quintic or quarticpolynomial functions), such that the curve is continuous along the curve(e.g., a derivative at the joint of two adjacent segments isobtainable). In the field of autonomous driving, the polynomial curvefrom a starting point to an end point is divided into a number ofsegments (or pieces), each segment corresponding to a control point (orreference point). Such a segmented polynomial curve is referred to as apiecewise polynomial. When optimizing the piecewise polynomial, a set ofjoint constraints and a set of boundary constraints between two adjacentsegments have to be satisfied, in addition to the set of initial stateconstraints and end state constraints.

The set of joint constraints includes positions (x, y), speed, headingdirection, and acceleration of the adjacent segments have to beidentical. For example, the ending position of a first segment (e.g.,leading segment) and the starting position of a second segment (e.g.,following segment) have to be identical or within a predeterminedproximity. The speed, heading direction, and acceleration of the endingposition of the first segment and the corresponding speed, headingdirection, and acceleration of the starting position of the secondsegment have to be identical or within a predetermined range. Inaddition, each control point is associated with a predefined boundary(e.g., 0.2 meters left and right surrounding the control point). Thepolynomial curve has to go through each control point within itscorresponding boundary. When these two set of constraints are satisfiedduring the optimization, the polynomial curve representing a trajectoryshould be smooth and continuous. However, the above optimizationoperations may not yield a smooth reference line when the two referencelines are connected or stitched together, especially when the ADV istravelling at high speeds which may result in abrupt turns/movementsnoticeable to the passengers in the ADV.

According to one embodiment, while the system is controlling an ADVaccording to a trajectory generated from a first reference line (e.g.,first reference line segment), reference line generator 308 truncatesthe first reference line, for example, using some of the optimizationalgorithms 313, to generate a truncated reference line and connects asecond reference line (e.g., second reference line segment) to the endpoint of the truncated reference line to generate a third reference linethat includes the first truncated reference line and the secondreference line The first truncated reference line and the secondreference line are smoothly connected with more flexibility, which willbe described in more detail below. The third reference line is thenutilized to autonomous drive the ADV.

With reference to FIG. 4, an ADV 402 is travelling along a trajectorygenerated from a first reference line 404 (also referred to as a firstreference line segment) which includes a beginning reference point 411and an ending reference point 414. For example, the first reference line404 may be 90 meters long and has a first set of constraints. Forexample, the first set of constraints may include that each referencepoint along the first reference line 404 satisfies inequalityconstraints such as a heading direction of ±0.01 degrees and apredefined boundary such as 20 centimeters. The first reference line404, via reference line generator 308, is then truncated by removing anend section 421 of the first reference line 404 to generate a truncatedreference line 406 which includes an end reference point 412.

In one embodiment, about 10 percent of the total length of the firstreference line 404 is removed. For example, the end section 421 that isremoved from the first reference line 404 is about 10 meters resultingin the truncated reference line 406 being 80 meters long. A second setof constraints, via reference line generator 308, is then obtained forthe end reference point 412 which is different and more stringent thanthe first set of constraints for the first reference line 404. In oneembodiment, the second set of constraints for the end reference point412 includes equality constraints such as a heading direction of ±0degrees and a predefined boundary such as zero centimeters. In otherwords, the ADV 402 travels through the end reference point 412 with thesame heading direction and no deviation from the (x, y) coordinates ofthe end reference point 412. The second set of constraints (“hardconstraints”) associated with the end reference point 412 makes thesmoothed results not able to move free.

For example, end reference point 412 has been restrained with itslocation and heading so that it cannot move free. End reference point412 acts as the end point of smoothed truncated reference line 406 andthe beginning point of smoothed reference line 2 (e.g., second referenceline 408). Since it cannot move free, when connecting the two smoothedreference lines together (e.g., truncated reference line 406 and secondreference line 408), the connection point is continuous in terms of itsx,y position and heading. The truncated reference line 406 now has noconstraints. Since the end section 421 has been removed from the firstreference line 404, no constraints are applied to the removed endsection of 10 meters allowing the end reference point 412 greaterflexibility in connecting with a second reference line 408.

Continuing with FIG. 4, second reference line 408 (also referred to as asecond reference line segment), via reference line generator 308, havingreference points 416 and 418 is generated and connected with thetruncated reference line 406. Since the removed end section has noconstraints associated with it, the second reference line 408 issmoothly connected to the end reference point 412 of the truncatedreference line 406. For example, the second reference line 408 may be 90meters long as the first reference line and may have the sameconstraints (i.e., the first set of constraints) as the first referenceline 404. The reference points 412 and 416 are then connected orstitched together to form a new stitch point/reference point 420 whichsmoothly connects the truncated reference line 406 to the secondreference line 408 to form a new reference line (e.g., a third referenceline) as shown in FIG. 4. The third reference line would include thetruncated first reference line 406 and second reference line 408connected with each other. The third reference line can then be utilizedto autonomous drive the ADV for the next driving cycle. The abovedescribed process is then repeated for successive reference lines, onefor each planning cycle, such that the ADV 402 continues to travel alongthe path using the references lines now connected together and smoothedusing the algorithms described above in order to reach its destinationlocation.

FIG. 5 is a flow diagram illustrating a process of generating areference line for controlling an autonomous driving vehicle accordingto one embodiment. Process 500 may be performed by processing logicwhich may include software, hardware, or a combination thereof. Forexample, process 500 may be performed in part by reference linegenerator 308. Referring to FIG. 5, in operation 502, processing logiccontrols an autonomous driving vehicle to move along a road according toa first reference line, the first reference line having a first set ofconstraints. In operation 504, while the autonomous driving vehicle ismoving along the road according to the first reference line, inoperation 506, via reference line generator 308, truncating the firstreference line by removing an end section of the first reference line togenerate a truncated first reference line including an end referencepoint is performed. The process further includes, in operation 508,obtaining a second set of constraints for the end reference point, inoperation 510, obtaining a second reference line to be used by theautonomous driving vehicle, the second reference line having the firstset of constraints, and in operation 512, connecting the secondreference line to the end reference point of the truncated referenceline to allow the autonomous driving vehicle to move along the road. Inone embodiment, the process further includes determining an amount ofthe end section of the first reference line to be removed based on aminimum or least curvature change within the end section. For example,some smoothing algorithms cannot set constraints for curvature whichcauses discontinuity problems at the curvature level. The fixedconnection point (e.g, point 414 in first reference line 404) is movedto the point (ranging from 5 m to 15 m from point 414) which has thesmallest curvature′ (indicating the change of curvature (derivative ofcurvature) at this point is small that no constraint was given).Connecting two reference lines at that point which has the smallestcurvature′ tends to produce a smaller discontinuity between thecurvatures which are joined.

In one embodiment, the second set of constraints includes equalityconstraints including a heading direction of the autonomous drivingvehicle. In one embodiment, the second set of constraints includesequality constraints including (x, y) coordinates of the autonomousdriving vehicle.

Note that some or all of the components as shown and described above maybe implemented in software, hardware, or a combination thereof. Forexample, such components can be implemented as software installed andstored in a persistent storage device, which can be loaded and executedin a memory by a processor (not shown) to carry out the processes oroperations described throughout this application. Alternatively, suchcomponents can be implemented as executable code programmed or embeddedinto dedicated hardware such as an integrated circuit (e.g., anapplication specific IC or ASIC), a digital signal processor (DSP), or afield programmable gate array (FPGA), which can be accessed via acorresponding driver and/or operating system from an application.Furthermore, such components can be implemented as specific hardwarelogic in a processor or processor core as part of an instruction setaccessible by a software component via one or more specificinstructions.

FIG. 6 is a block diagram illustrating an example of a data processingsystem which may be used with one embodiment of the disclosure. Forexample, system 1500 may represent any of data processing systemsdescribed above performing any of the processes or methods describedabove, such as, for example, perception and planning system 110,reference line generator 308 or any of servers 103-104 of FIG. 1. System1500 can include many different components. These components can beimplemented as integrated circuits (ICs), portions thereof, discreteelectronic devices, or other modules adapted to a circuit board such asa motherboard or add-in card of the computer system, or as componentsotherwise incorporated within a chassis of the computer system.

Note also that system 1500 is intended to show a high level view of manycomponents of the computer system. However, it is to be understood thatadditional components may be present in certain implementations andfurthermore, different arrangement of the components shown may occur inother implementations. System 1500 may represent a desktop, a laptop, atablet, a server, a mobile phone, a media player, a personal digitalassistant (PDA), a Smartwatch, a personal communicator, a gaming device,a network router or hub, a wireless access point (AP) or repeater, aset-top box, or a combination thereof. Further, while only a singlemachine or system is illustrated, the term “machine” or “system” shallalso be taken to include any collection of machines or systems thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein.

In one embodiment, system 1500 includes processor 1501, memory 1503, anddevices 1505-1508 connected via a bus or an interconnect 1510. Processor1501 may represent a single processor or multiple processors with asingle processor core or multiple processor cores included therein.Processor 1501 may represent one or more general-purpose processors suchas a microprocessor, a central processing unit (CPU), or the like. Moreparticularly, processor 1501 may be a complex instruction set computing(CISC) microprocessor, reduced instruction set computing (RISC)microprocessor, very long instruction word (VLIW) microprocessor, orprocessor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processor 1501 may alsobe one or more special-purpose processors such as an applicationspecific integrated circuit (ASIC), a cellular or baseband processor, afield programmable gate array (FPGA), a digital signal processor (DSP),a network processor, a graphics processor, a communications processor, acryptographic processor, a co-processor, an embedded processor, or anyother type of logic capable of processing instructions.

Processor 1501, which may be a low power multi-core processor socketsuch as an ultra-low voltage processor, may act as a main processingunit and central hub for communication with the various components ofthe system. Such processor can be implemented as a system on chip (SoC).Processor 1501 is configured to execute instructions for performing theoperations and steps discussed herein. System 1500 may further include agraphics interface that communicates with optional graphics subsystem1504, which may include a display controller, a graphics processor,and/or a display device.

Processor 1501 may communicate with memory 1503, which in one embodimentcan be implemented via multiple memory devices to provide for a givenamount of system memory. Memory 1503 may include one or more volatilestorage (or memory) devices such as random access memory (RAM), dynamicRAM (DRAM), synchronous DRAM (SDRAM), static RAM (SRAM), or other typesof storage devices. Memory 1503 may store information includingsequences of instructions that are executed by processor 1501, or anyother device. For example, executable code and/or data of a variety ofoperating systems, device drivers, firmware (e.g., input output basicsystem or BIOS), and/or applications can be loaded in memory 1503 andexecuted by processor 1501. An operating system can be any kind ofoperating systems, such as, for example, Robot Operating System (ROS),Windows® operating system from Microsoft®, Mac OS®/iOS® from Apple,Android® from Google®, LINUX, UNIX, or other real-time or embeddedoperating systems.

System 1500 may further include IO devices such as devices 1505-1508,including network interface device(s) 1505, optional input device(s)1506, and other optional IO device(s) 1507. Network interface device1505 may include a wireless transceiver and/or a network interface card(NIC). The wireless transceiver may be a WiFi transceiver, an infraredtransceiver, a Bluetooth™ transceiver, a WiMax transceiver, a wirelesscellular telephony transceiver, a satellite transceiver (e.g., a globalpositioning system (GPS) transceiver), or other radio frequency (RF)transceivers, or a combination thereof. The NIC may be an Ethernet card.

Input device(s) 1506 may include a mouse, a touch pad, a touch sensitivescreen (which may be integrated with display device 1504), a pointerdevice such as a stylus, and/or a keyboard (e.g., physical keyboard or avirtual keyboard displayed as part of a touch sensitive screen). Forexample, input device 1506 may include a touch screen controller coupledto a touch screen. The touch screen and touch screen controller can, forexample, detect contact and movement or break thereof using any of aplurality of touch sensitivity technologies, including but not limitedto capacitive, resistive, infrared, and surface acoustic wavetechnologies, as well as other proximity sensor arrays or other elementsfor determining one or more points of contact with the touch screen.

IO devices 1507 may include an audio device. An audio device may includea speaker and/or a microphone to facilitate voice-enabled functions,such as voice recognition, voice replication, digital recording, and/ortelephony functions. Other 10 devices 1507 may further include universalserial bus (USB) port(s), parallel port(s), serial port(s), a printer, anetwork interface, a bus bridge (e.g., a PCI-PCI bridge), sensor(s)(e.g., a motion sensor such as an accelerometer, gyroscope, amagnetometer, a light sensor, compass, a proximity sensor, etc.), or acombination thereof. Devices 1507 may further include an imagingprocessing subsystem (e.g., a camera), which may include an opticalsensor, such as a charged coupled device (CCD) or a complementarymetal-oxide semiconductor (CMOS) optical sensor, utilized to facilitatecamera functions, such as recording photographs and video clips. Certainsensors may be coupled to interconnect 1510 via a sensor hub (notshown), while other devices such as a keyboard or thermal sensor may becontrolled by an embedded controller (not shown), dependent upon thespecific configuration or design of system 1500.

To provide for persistent storage of information such as data,applications, one or more operating systems and so forth, a mass storage(not shown) may also couple to processor 1501. In various embodiments,to enable a thinner and lighter system design as well as to improvesystem responsiveness, this mass storage may be implemented via a solidstate device (SSD). However in other embodiments, the mass storage mayprimarily be implemented using a hard disk drive (HDD) with a smalleramount of SSD storage to act as a SSD cache to enable non-volatilestorage of context state and other such information during power downevents so that a fast power up can occur on re-initiation of systemactivities. Also a flash device may be coupled to processor 1501, e.g.,via a serial peripheral interface (SPI). This flash device may providefor non-volatile storage of system software, including BIOS as well asother firmware of the system.

Storage device 1508 may include computer-accessible storage medium 1509(also known as a machine-readable medium, machine-readable storagemedium or a computer-readable medium, all of which may benon-transitory) on which is stored one or more sets of instructions orsoftware (e.g., module, unit, and/or logic 1528) embodying any one ormore of the methodologies or functions described herein. Processingmodule/unit/logic 1528 may represent any of the components describedabove, such as, for example, planning module 305, control module 306,reference line generator 308. Processing module/unit/logic 1528 may alsoreside, completely or at least partially, within memory 1503 and/orwithin processor 1501 during execution thereof by data processing system1500, memory 1503 and processor 1501 also constitutingmachine-accessible storage media. Processing module/unit/logic 1528 mayfurther be transmitted or received over a network via network interfacedevice 1505.

Computer-readable storage medium 1509 may also be used to store the somesoftware functionalities described above persistently. Whilecomputer-readable storage medium 1509 is shown in an exemplaryembodiment to be a single medium, the term “computer-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store the one or more sets of instructions. The terms“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing or encoding a set of instructions forexecution by the machine and that cause the machine to perform any oneor more of the methodologies of the present disclosure. The term“computer-readable storage medium” shall accordingly be taken toinclude, but not be limited to, solid-state memories, and optical andmagnetic media, or any other non-transitory machine-readable medium.

Processing module/unit/logic 1528, components and other featuresdescribed herein can be implemented as discrete hardware components orintegrated in the functionality of hardware components such as ASICS,FPGAs, DSPs or similar devices. In addition, processingmodule/unit/logic 1528 can be implemented as firmware or functionalcircuitry within hardware devices. Further, processing module/unit/logic1528 can be implemented in any combination hardware devices and softwarecomponents.

Note that while system 1500 is illustrated with various components of adata processing system, it is not intended to represent any particulararchitecture or manner of interconnecting the components; as suchdetails are not germane to embodiments of the present disclosure. Itwill also be appreciated that network computers, handheld computers,mobile phones, servers, and/or other data processing systems which havefewer components or perhaps more components may also be used withembodiments of the disclosure.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as those set forth in the claims below, refer to the actionand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

Embodiments of the disclosure also relate to an apparatus for performingthe operations herein. Such a computer program is stored in anon-transitory computer readable medium. A machine-readable mediumincludes any mechanism for storing information in a form readable by amachine (e.g., a computer). For example, a machine-readable (e.g.,computer-readable) medium includes a machine (e.g., a computer) readablestorage medium (e.g., read only memory (“ROM”), random access memory(“RAM”), magnetic disk storage media, optical storage media, flashmemory devices).

The processes or methods depicted in the preceding figures may beperformed by processing logic that comprises hardware (e.g. circuitry,dedicated logic, etc.), software (e.g., embodied on a non-transitorycomputer readable medium), or a combination of both. Although theprocesses or methods are described above in terms of some sequentialoperations, it should be appreciated that some of the operationsdescribed may be performed in a different order. Moreover, someoperations may be performed in parallel rather than sequentially.

Embodiments of the present disclosure are not described with referenceto any particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof embodiments of the disclosure as described herein.

In the foregoing specification, embodiments of the disclosure have beendescribed with reference to specific exemplary embodiments thereof. Itwill be evident that various modifications may be made thereto withoutdeparting from the broader spirit and scope of the disclosure as setforth in the following claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense.

What is claimed is:
 1. A computer-implemented method for generating areference line for operating an autonomous driving vehicle, the methodcomprising: determining a truncation point on a first reference linehaving a smallest curvature change within a distance from a first endreference point of the first reference line, the first reference linebeing generated based on a first set of constraints; truncating thefirst reference line by removing an end section of the first referenceline from the truncation point to the first end reference point togenerate a truncated first reference line including a second endreference point, wherein the second end reference point is associatedwith a second set of constraints; obtaining a second reference line tobe used by the autonomous driving vehicle, the second reference linebeing generated based on the first set of constraints; connecting thesecond reference line to the second end reference point of the truncatedfirst reference line by satisfying the second set of constraintsassociated with the second end reference point of the truncated firstreference line; and controlling the autonomous driving vehicle along thetruncated first reference line and the second reference line asconnected.
 2. The method of claim 1, wherein the second set ofconstraints is different than the first set of constraints.
 3. Themethod of claim 2, wherein the second set of constraints is morestringent than the first set of constraints.
 4. The method of claim 2,wherein the second set of constraints comprises equality constraintscomprising a heading direction of the autonomous driving vehicle.
 5. Themethod of claim 2, wherein the second set of constraints comprisesequality constraints comprising (x, y) coordinates of the autonomousdriving vehicle.
 6. The method of claim 1, wherein the end sectionremoved from the first reference line is about ten percent of a totallength of the first reference line.
 7. A non-transitory machine-readablemedium having instructions stored therein, which when executed by aprocessor, cause the processor to perform operations, the operationscomprising: determining a truncation point on a first reference linehaving a smallest curvature change within a distance from a first endreference point of the first reference line, the first reference linebeing generated based on a first set of constraints and to be used tooperate an autonomous driving vehicle; truncating the first referenceline by removing an end section of the first reference line from thetruncation point to the first end reference point to generate atruncated first reference line including a second end reference point,wherein the second end reference point is associated with a second setof constraints; obtaining a second reference line to be used by theautonomous driving vehicle, the second reference line being generatedbased on the first set of constraints; connecting the second referenceline to the second end reference point of the truncated first referenceline by satisfying the second set of constraints associated with thesecond end reference point of the truncated first reference line; andcontrolling the autonomous driving vehicle along the truncated firstreference line and the second reference line as connected.
 8. Themachine-readable medium of claim 7, wherein the second set ofconstraints is different than the first set of constraints.
 9. Themachine-readable medium of claim 8, wherein the second set ofconstraints is more stringent than the first set of constraints.
 10. Themachine-readable medium of claim 8, wherein the second set ofconstraints comprises equality constraints comprising a headingdirection of the autonomous driving vehicle.
 11. The machine-readablemedium of claim 8, wherein the second set of constraints comprisesequality constraints comprising (x, y) coordinates of the autonomousdriving vehicle.
 12. The machine-readable medium of claim 7, wherein theend section removed from the first reference line is about ten percentof a total length of the first reference line.
 13. A data processingsystem comprising: a processor; and a memory coupled to the processor tostore instructions, which when executed by the processor, cause theprocessor to perform operations, the operations including: determining atruncation point on a first reference line having a smallest curvaturechange within a distance from a first end reference point of the firstreference line, the first reference line being generated based on afirst set of constraints and to be used for operating an autonomousdriving vehicle, truncating the first reference line by removing an endsection of the first reference line from the truncation point to thefirst end reference point to generate a truncated first reference lineincluding a second end reference point, wherein the second end referencepoint is associated with a second set of constraints, obtaining a secondreference line to be used by the autonomous driving vehicle, the secondreference line being generated based on the first set of constraints,connecting the second reference line to the second end reference pointof the truncated first reference line by satisfying the second set ofconstraints associated with the second end reference point of thetruncated first reference line, and controlling the autonomous drivingvehicle along the truncated first reference line and the secondreference line as connected.
 14. The data processing system of claim 13,wherein the second set of constraints is different than the first set ofconstraints.
 15. The data processing system of claim 14, wherein thesecond set of constraints is more stringent than the first set ofconstraints.
 16. The data processing system of claim 14, wherein thesecond set of constraints comprises equality constraints comprising aheading direction and (x, y) coordinates of the autonomous drivingvehicle.
 17. The data processing system of claim 13, wherein the endsection removed from the first reference line is about ten percent of atotal length of the first reference line.